Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems Article Swipe
Nebras Sobahi
,
Ahteshamul Haque
,
Varaha Satya Bharath Kurukuru
,
Md. Mottahir Alam
,
Asif Irshad Khan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.028340
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2022.028340
Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system. The localized fault condition is combined with a parallel operation of adaptive neurofuzzy inference units (ANFIUs) to develop a power mismatch-based control unit (PMCU) for improving the power output of the GCPV. To develop the proposed framework, a 10-kW single-phase GCPV is simulated for training the NN-based anomaly detection approach with 14 deviation signals. Further, the developed algorithm is combined with the PMCU implemented with the experimental setup of GCPV. The results identified 98.2% training accuracy and 43000 observations/sec prediction speed for the trained classifier, and improved power output with reduced voltage and current harmonics for the grid-connected PV operation.
Related Topics
Concepts
Photovoltaic system
Computer science
Artificial neural network
Fault (geology)
Harmonics
Maximum power point tracking
Grid
Grid-connected photovoltaic power system
Electric power system
Renewable energy
Fault detection and isolation
Electronic engineering
Power (physics)
Control engineering
Engineering
Reliability engineering
Voltage
Inverter
Electrical engineering
Artificial intelligence
Seismology
Geometry
Quantum mechanics
Geology
Mathematics
Physics
Actuator
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2022.028340
- https://file.techscience.com/files/cmc/2023/74-3/TSP_CMC_28340/TSP_CMC_28340.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313241475
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313241475Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/cmc.2022.028340Digital Object Identifier
- Title
-
Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic SystemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
-
Nebras Sobahi, Ahteshamul Haque, Varaha Satya Bharath Kurukuru, Md. Mottahir Alam, Asif Irshad KhanList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2022.028340Publisher landing page
- PDF URL
-
https://file.techscience.com/files/cmc/2023/74-3/TSP_CMC_28340/TSP_CMC_28340.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://file.techscience.com/files/cmc/2023/74-3/TSP_CMC_28340/TSP_CMC_28340.pdfDirect OA link when available
- Concepts
-
Photovoltaic system, Computer science, Artificial neural network, Fault (geology), Harmonics, Maximum power point tracking, Grid, Grid-connected photovoltaic power system, Electric power system, Renewable energy, Fault detection and isolation, Electronic engineering, Power (physics), Control engineering, Engineering, Reliability engineering, Voltage, Inverter, Electrical engineering, Artificial intelligence, Seismology, Geometry, Quantum mechanics, Geology, Mathematics, Physics, ActuatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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